Magneto-Acousto-Electrical Tomography (MAET) is a hybrid imaging method that combines advantages of ultrasound imaging and electrical impedance tomography to image the electrical conductivity of biological tissues. In practical applications, different tissue or disease organization display various conductivity traits. However, the conductivity map consists of overlapping signals measured at multiple locations, the reconstruction results are affected by noise, which results in blurred reconstruction boundaries, low contrast, and irregular artifact distributions. To improve the image resolution and reduce noise of MAET, a dataset of conductivity maps reconstructed from MAET was established, dubbed MAET-IMAGE. Based on this dataset, we proposed a MAET tomography segmentation network based on the Segment Anything Model (SAM), termed as MAET-SAM. Specifically, we froze the encoder weights of SAM to extract rich feature information of image and design, an adaptive decoder with no prompts. In the end, an end-to-end segmentation model for specific MAET images with MAET-IMAGE was proposed. Qualitative and quantitative experiments demonstrated that MAET-SAM outperformed traditional segmentation methods and segmentation models with initial weights in terms of MAET image segmentation performance, bringing new breakthroughs and advancements to the field of medical imaging analysis and clinical diagnosis.